Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Pedagogical pattern selection strategies
Neural Networks
Machine Learning - Special issue on applications in molecular biology
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A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (protein identification resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (ribosomal database project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences into neural input vectors, a SVD (singular value decomposition) method to compress vectors, and a term weighting method to extract motif information. The neural networks used were three-layered, feed-forward networks that employed backpropagation or counter-propagation learning paradigms. The system runs faster by one to two orders of magnitude than existing method and has a sensitivity of 85 to 100%. The gene classification artificial neural system is available on the Internet, and may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.